Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add roedyrustam/vibes-plug --skill data-pipeline-etl-expertgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/roedyrustam/vibes-plug/data-pipeline-etl-expert)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/data-pipeline-etl-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/data-pipeline-etl-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/data-pipeline-etl-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/data-pipeline-etl-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.01170 |
| Opus 5 | $0.00042 | $0.00585 |
| Sonnet 5 | $0.00017 | $0.00234 |
| Haiku 4.5 | $0.00008 | $0.00117 |
Grade A, and why
data-pipeline-etl-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline & ETL Expert
English
Description
A specialized skill for building robust data architectures, Analytics Engineering, and ETL (Extract, Transform, Load) or ELT pipelines. It covers modern data stack orchestration (Airflow, Dagster), transformation tools (dbt), and high-performance OLAP databases (BigQuery, Snowflake, ClickHouse, DuckDB).
Trigger Conditions
- When designing reporting dashboards or analytics infrastructure for a SaaS.
- When moving large volumes of data from transactional databases (PostgreSQL/MySQL) to a data warehouse.
- When the user asks about "dbt", "Airflow", "ELT", or "Analytics Engineering".
- When building local or edge analytics using DuckDB.
Core Architectural Guidelines
1. ELT over ETL
Prefer Extract-Load-Transform (ELT) over traditional ETL.
- Extract & Load: Use tools like Airbyte or Fivetran to dump raw data directly into the Data Warehouse.
- Transform: Perform transformations inside the Data Warehouse using SQL (via dbt) to leverage the warehouse's massive compute power.
2. Analytics Engineering with dbt
Treat SQL like software engineering.
- Use
dbt(Data Build Tool) to version control your SQL transformations. - Implement tests (
not_null,unique) on critical tables. - Use Jinja templating in dbt to DRY up complex SQL queries.
3. Data Orchestration (Airflow vs Dagster)
- Apache Airflow: The industry standard for scheduling and monitoring complex DAGs (Directed Acyclic Graphs). Best for Python-heavy teams.
- Dagster: A modern alternative focused on data assets rather than just tasks. Use Dagster when you want better local testing and asset-driven lineage.
4. OLAP Database Selection
- BigQuery / Snowflake: Best for massive scale, fully managed cloud data warehousing.
- ClickHouse: Best for real-time, sub-second analytical queries on massive event streams.
- DuckDB: Best for local analytics, embedded analytical pipelines, or processing parquets in edge environments (Node.js/Python).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed 3e19bd0c55cb
- 12d ago First seen · 85 lines · 83 tokens per session scan A 4b6f8bf9e71a
data-pipeline-etl-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (53 stars, last pushed today), licensed MIT. It adds 83 tokens to every session and 1,170 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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